帕金森病诊断中可穿戴心电图设备测量的心率变异性及其与神经影像生物标志物的关联：病例对照研究数据表1

数据集获取（Figshare）

• 源站标题：Data Sheet 1_Implications of heart rate variability measured using wearable electrocardiogram devices in diagnosing Parkinson’s disease and its association with neuroimaging biomarkers: a case-control study.docx
• DOI：10.3389/fnagi.2025.1530240.s001
• 发布日期：2025-05-12
• 文件数：1 个
• 文件总体积：621.68 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Implications_of_heart_rate_variability_measured_using_wearable_electrocardiogram_devices_in_diagnosing_Parkinson_s_disease_and_its_association_with_neuroimaging_biomarkers_a_case-control_study_docx/29036540

命令行下载

curl -L -o "Data Sheet 1_Implications of heart rate variability measured using wearable electrocardiogram devices in diagnosing Parkinson’s disease and its associ.docx" "https://ndownloader.figshare.com/files/54441368"

文件清单

• Data Sheet 1_Implications of heart rate variability measured using wearable electrocardiogram devices in diagnosing Parkinson’s disease and its associ.docx（621.68 KB）

文件总体积 621.68 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

IntroductionHeart rate variability (HRV) reflects cardiac autonomic regulation, and reduced HRV is associated with Parkinson’s disease (PD). However, studies regarding the implications of HRV measures for the clinical manifestations of PD have shown inconclusive results. We examined the relationship between HRV measures obtained via long-term monitoring using a wearable electrocardiogram (ECG) device and the diagnosis and clinical characteristics of PD.
MethodsSeventeen controls and 20 patients with PD were prospectively enrolled. The HRV measures were recorded using a wearable ECG device for up to 72 h. Time- and frequency-domain measures were derived from the HRV analysis, and their association with PD diagnosis and clinical features was investigated. We investigated their association with neuroimaging biomarkers using magnetic resonance imaging to explore the underlying neural correlates.
ResultsThe diagnosis of PD was associated with several HRV measures, including a decreased standard deviation of N-N intervals, standard deviation of all heart rates, and low-frequency (LF) power. Among these HRV measures, only LF power was associated with clinical features of PD. LF power was positively correlated with the tremor sub-score (r = 0.500; p = 0.035) and negatively associated with the left (r = −0.598; p = 0.024) and right (r = −0.693; p = 0.006) cerebellar hemispheres in patients with PD.
ConclusionLow-frequency power may be used as a biomarker for tremor-associated pathophysiology of PD. Moreover, a wearable ECG device with its capability for long-term monitoring might be a promising tool for diagnosing PD.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fnagi.2025.1530240.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Implications_of_heart_rate_variability_measured_using_wearable_electrocardiogram_devices_in_diagnosing_Parkinson_s_disease_and_its_association_with_neuroimaging_biomarkers_a_case-control_study_docx/29036540）。